1.Dyspnea.
Journal of the Korean Medical Association 2005;48(3):254-264
No abstract available.
Dyspnea*
2.Diagnostic Evaluation of Dyspnea.
Journal of the Korean Medical Association 1997;40(4):493-500
No abstract available.
Dyspnea*
3.Dyspnea.
Journal of the Korean Medical Association 1997;40(2):236-241
No abstract available.
Dyspnea*
5.Two cases of hypothyroidism presenting with dyspnea.
Shin Ho BANG ; Kyoung Sook WON ; Young Suk OH ; Won PARK ; Hong Soon LEE
Journal of Korean Society of Endocrinology 1992;7(3):295-299
No abstract available.
Dyspnea*
;
Hypothyroidism*
6.Symptom Based Echocardiographic Approach: Dyspnea.
Journal of the Korean Society of Echocardiography 2004;12(1):5-9
No abstract available.
Dyspnea*
;
Echocardiography*
7.Differential Diagnosis of Dyspnea.
Korean Journal of Medicine 1998;55(4):618-627
No abstract available.
Diagnosis, Differential*
;
Dyspnea*
8.Differential diagnosis and treatment of dyspnea.
Korean Journal of Medicine 2009;76(4):425-430
No abstract available.
Diagnosis, Differential
;
Dyspnea
9.Exertional Dyspnea, Arterial Oxygen Desaturation and ECG Abnormalities.
Journal of the Korean Medical Association 1998;41(2):204-211
No abstract available.
Dyspnea*
;
Electrocardiography*
;
Oxygen*
10.Variable Threshold based Feature Selection using Spatial Distribution of Data.
Chang Sik SON ; A Mi SHIN ; Young Dong LEE ; Hee Joon PARK ; Hyoung Seob PARK ; Yoon Nyun KIM
Journal of Korean Society of Medical Informatics 2009;15(4):475-481
OBJECTIVE: In processing high dimensional clinical data, choosing the optimal subset of features is important, not only for reduce the computational complexity but also to improve the value of the model constructed from the given data. This study proposes an efficient feature selection method with a variable threshold. METHODS: In the proposed method, the spatial distribution of labeled data, which has non-redundant attribute values in the overlapping regions, was used to evaluate the degree of intra-class separation, and the weighted average of the redundant attribute values were used to select the cut-off value of each feature. RESULTS: The effectiveness of the proposed method was demonstrated by comparing the experimental results for the dyspnea patients' dataset with 11 features selected from 55 features by clinical experts with those obtained using seven other classification methods. CONCLUSION: The proposed method can work well for clinical data mining and pattern classification applications.
Data Mining
;
Dyspnea
Result Analysis
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